A knowledge regularized hierarchical approach for emotion cause analysis

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure

Recognizing Emotions in Short Texts

Tese de mestrado, Ciência Cognitiva, Universidade de Lisboa, Faculdade de Ciências, 2022O reconhecimento automático de emoções em texto é uma tarefa que mobiliza as áreas de processamento de linguagem natural e de computação afetiva, para as quais se pode contar com o especial contributo de disciplinas da Ciência Cognitiva como Inteligência Artificial e Ciência da Computação, Linguística e Psicologia. Visa, sobretudo, a deteção e interpretação de emoções humanas através da sua expressão na forma escrita por sistemas computacionais. A interação entre processos afetivos e cognitivos, o papel essencial que as emoções desempenham nas interações interpessoais e a crescente utilização de comunicação escrita online nos dias de hoje fazem com que o reconhecimento de emoções de forma automática seja cada vez mais importante, nomeadamente em áreas como saúde mental, interação pessoa-computador, ciência política ou marketing. A língua inglesa tem sido o maior alvo de estudo no que diz respeito ao reconhecimento de emoções em textos, sendo que ainda existe pouco trabalho desenvolvido para a língua portuguesa. Assim, existe uma necessidade em expandir o trabalho feito para a língua inglesa para o português. Esta dissertação tem como objetivo a comparação de dois métodos distintos de aprendizagem profunda resultantes dos avanços na área de Inteligência Artificial para detetar e classificar de forma automática estados emocionais discretos em textos escritos em língua portuguesa. Para tal, a abordagem de classificação de Polignano et al. (2019) baseada em redes de aprendizagem profunda como Long Short-Term Memory bidirecionais e redes convolucionais mediadas por um mecanismo de atenção será replicada para a língua inglesa e será reproduzida para a língua portuguesa. Para a língua inglesa, será utilizado o conjunto de dados da tarefa 1 do SemEval-2018 (Mohammad et al., 2018) tal como na experiência original, que considera quatro emoções discretas: raiva, medo, alegria e tristeza. Para a língua portuguesa, tendo em consideração a falta de conjuntos de dados disponíveis anotados relativamente a emoções, será efetuada uma recolha de dados a partir da rede social Twitter recorrendo a hashtags com conteúdo associado a uma emoção específica para determinar a emoção subjacente ao texto de entre as mesmas quatro emoções presentes no conjunto de dados da língua inglesa que será utilizado. De acordo com experiências realizadas por Mohammad & Kiritchenko (2015), este método de recolha de dados é consistente com a anotação de juízes humanos treinados. Tendo em conta a rápida e contínua evolução dos métodos de aprendizagem profunda para o processamento de linguagem natural e o estado da arte estabelecido por métodos recentes em tarefas desta área tal como o modelo pré-treinado BERT (Bidirectional Encoder Representations from Tranformers) (Devlin et al., 2019), será também aplicada esta abordagem para a tarefa de reconhecimento de emoções para as duas línguas em questão, utilizando os mesmos conjuntos de dados das experiências anteriores. Enquanto a abordagem de Polignano et al. teve um melhor desempenho nas experiências que realizámos com dados em inglês, com diferenças de F1-score de 0.02, o melhor resultado obtido nas experiências com dados na língua portuguesa foi com o modelo BERT, obtendo um resultado máximo de F1-score de 0.6124.Automatic emotion recognition from text is a task that mobilizes the areas of natural language processing and affective computing counting with the special contribution of Cognitive Science subjects such as Artificial Intelligence and Computer Science, Linguistics and Psychology. It aims at the detection and interpretation of human emotions expressed in the written form by computational systems. The interaction of affective and cognitive processes, the essential role that emotions play in interpersonal interactions and the currently increasing use of written communication online make automatic emotion recognition progressively important, namely in areas such as mental healthcare, human-computer interaction, political science, or marketing. The English language has been the main target of studies in emotion recognition in text and the work developed for the Portuguese language is still scarce. Thus, there is a need to expand the work developed for English to Portuguese. The goal of this dissertation is to present and compare two distinct deep learning methods resulting from the advances in Artificial Intelligence to automatically detect and classify discrete emotional states in texts written in Portuguese. For this, the classification approach of Polignano et al. (2019) based on deep learning networks such as bidirectional Long Short-Term Memory and convolutional networks mediated by a self-attention level will be replicated for English and it will be reproduced for Portuguese. For English, the SemEval-2018 task 1 dataset (Mohammad et al., 2018) will be used, as in the original experience, and it considers four discrete emotions: anger, fear, joy, and sadness. For Portuguese, considering the lack of available emotionally annotated datasets, data will be collected from the social network Twitter using hashtags associated to a specific emotional content to determine the underlying emotion of the text from the same four emotions present in the English dataset. According to experiments carried out by Mohammad & Kiritchenko (2015), this method of data collection is consistent with the annotation of trained human judges. Considering the fast and continuous evolution of deep learning methods for natural language processing and the state-of-the-art results achieved by recent methods in tasks in this area such as the pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019), this approach will also be applied to the task of emotion recognition for both languages using the same datasets from the previous experiments. It is expected to draw conclusions about the adequacy of these two presented approaches in emotion recognition and to contribute to the state of the art in this task for the Portuguese language. While the approach of Polignano et al. had a better performance in the experiments with English data with a difference in F1 scores of 0.02, for Portuguese we obtained the best result with BERT having a maximum F1 score of 0.6124

Assessing Trust and Veracity of Data in Social Media

Social media highly impacts our knowledge and perception of the world. With the tremendous amount of data that is circulating in social media and initiated by a vast number of users from all over the world, extracting useful information from such data and assessing its veracity has become much more challenging. Data veracity refers to the trustworthiness and certainty of data. The challenges of handling textual data in social media have raised the need for efficient tools to extract, understand, and assess the veracity of information circulating in social media at a given time. In this thesis, we present three research problems to address major challenges of handling textual data in social media. First, overwhelming the user with huge volumes of short, noisy, and unstructured textual data complicates the task of understanding what topics are discussed by users in micro-blogging websites. Topic models were proposed to automatically learn a set of keywords that better describe each topic covered by a large corpus of text documents to enable fast and effective browsing and exploration of its contents. However, in order for the results of topic modeling algorithms to be useful, these results have to be interpretable. Applying topic models to social media data to get meaningful results is not a trivial task. In this thesis, we study the problem of improving interpretation of topic modeling of micro-posts in social media. We propose a new method that incorporates topic modeling, a lexical database, and the set of hashtags available in the corpus of micro-posts to produce a higher quality representation of each extracted topic. Extensive experiments on two real-life datasets collected from Twitter show that our method outperforms the state-of-the-art model in terms of perplexity, topics' coherence, and their quality. Second, the nature and flexibility of social media facilitate the process of posting unverified information, especially during the rapid diffusion of breaking news. Efficiently detecting and acting upon unverified breaking news rumors throughout social media is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this thesis, we study the problem of assessing the veracity of information contained in micro-posts regarding emerging stories and topics of breaking news. We propose a new approach that jointly learns word embeddings and trains a neural network model with two different objectives to automatically identify unverified micro-posts spreading in social media during breaking news. Extensive experiments on real-life datasets show that our proposed model outperforms the state-of-the-art classifier as well as other baseline classifiers in terms of precision, recall, and F1. Finally, the uncertainty and chaos associated with hot and sensitive breaking news and emergencies facilitate the explosive spread of high-engaging breaking news rumors that might be extremely damaging. In such a case, authorities have to prioritize the rumors verification process and act upon high-engaging breaking news rumors quickly to reduce their damaging consequences. However, this is an extremely challenging task. In this thesis, we study the problem of identifying rumors micro-posts that are most likely to become viral and achieve high engagement rates among recipients in social media during breaking news. We propose a multi-task neural network to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction. Extensive experiments on real-life datasets show that the performance of our joint learning model outperforms other baseline classifiers in terms of precision, recall, and F1 and is capable of identifying high-engaging breaking news rumors with high accuracy

Computational Sarcasm Analysis on Social Media: A Systematic Review

Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone. Because of the obscure nature of sarcasm in textual data, detecting it is difficult and of great interest to the sentiment analysis research community. Though the research in sarcasm detection spans more than a decade, some significant advancements have been made recently, including employing unsupervised pre-trained transformers in multimodal environments and integrating context to identify sarcasm. In this study, we aim to provide a brief overview of recent advancements and trends in computational sarcasm research for the English language. We describe relevant datasets, methodologies, trends, issues, challenges, and tasks relating to sarcasm that are beyond detection. Our study provides well-summarized tables of sarcasm datasets, sarcastic features and their extraction methods, and performance analysis of various approaches which can help researchers in related domains understand current state-of-the-art practices in sarcasm detection.Comment: 50 pages, 3 tables, Submitted to 'Data Mining and Knowledge Discovery' for possible publicatio